AI generation

AI generation is optional and off by default. When configured, Studio authors can describe an activity and generate HTML, CSS, JavaScript, a display name, and grading metadata. They can also refine the current content.

Generated fields are placed in the editor for review. Nothing is persisted until the author selects Save.

Configure an OpenAI-compatible endpoint

Add the setting to both LMS and CMS configuration under XBLOCK_SETTINGS:

XBLOCK_SETTINGS:
  InteractiveContentXBlock:
    ai:
      api_url: "https://api.openai.com/v1/chat/completions"
      api_key: "${INTERACTIVE_CONTENT_AI_API_KEY}"
      model: "gpt-4.1"
      timeout: 120
      temperature: 0.7
      max_tokens: 8192
      extra_headers: {}

The endpoint must implement the OpenAI-compatible chat completions response shape. api_url or a custom provider enables the Studio section. No AI SDK or additional Python dependency is installed by the XBlock.

Caution

Keep credentials in your deployment’s secret store. The example uses a placeholder environment reference; do not commit a real API key to Tutor config or source control.

Use a custom provider

Deployments can route generation through their own gateway, credential system, or usage accounting by configuring a dotted Python callable:

XBLOCK_SETTINGS:
  InteractiveContentXBlock:
    ai:
      provider: "myplugin.ai.generate_for_interactive_xblock"
      model: "deployment-specific-model"

The function receives keyword arguments:

def generate_for_interactive_xblock(*, prompt, config, context):
    # context contains usage_id, user_id, and username
    return raw_model_response

Return the raw model text in the format requested by the prompt: fenced html, css, js, and json blocks. The JSON metadata can include display_name and correct_answers.

Author workflow

1. Open Generate with AI in the Studio editor.
2. Describe one focused inline learning interaction.
3. Select Refine current content when you want to preserve and modify the current component.
4. Generate, then review all HTML, CSS, JavaScript, and grading fields.
5. Run Test Grading.
6. Save and test the published unit as a learner.

AI generation automatically enables grade publication because generated JavaScript is required to call submitInteraction() with correctness data.

Security and cost controls

• Only a user with course write permission in Studio can call the generation handler. It fails closed in the LMS, XBlock Workbench, and runtimes without the Studio permissions service.
• The API key stays on the server and is not sent to the browser.
• Author prompts are capped at 8,000 characters. Each current content field is capped at 40,000 characters during refinement, and the current title at 1,000.
• The generated response must contain HTML and JavaScript, call submitInteraction(), and keep <script> and <style> elements out of the HTML field.
• Generated code still runs as trusted author code in the LMS. Human review is required before publishing.

The default request timeout is 120 seconds, temperature is 0.7, and maximum output is 8,192 tokens. Set deployment-appropriate budgets and upstream rate limits; the XBlock does not provide per-user quotas.